Marker, method and product for identifying neurotoxicity caused by mixed exposure of perfluorinated compounds and application

Abstract

The invention belongs to the field of environmental damage judicial expertise or environmental forensic medicine, and particularly relates to a marker, a method and a product for identifying neurotoxicity caused by mixed exposure of perfluorinated compounds and application. The marker comprises a miRNA combination and / or a metabolite combination, the miRNA combination comprises 17 differential miRNAs, and the metabolite combination comprises 7 differential metabolites. The biomarker combination provided by the invention shows significant change in an exposed sample, shows excellent diagnostic efficiency, and can be used for early identification of neurotoxicity caused by mixed exposure of environmental concentration PFASs.

Description

Technical Field

[0001] The present invention belongs to the field of environmental damage forensic identification or environmental forensic medicine, and specifically relates to a marker, method, product and application for identifying neurotoxicity caused by mixed exposure to perfluorinated compounds. Background Art

[0002] Per- and polyfluoroalkyl substances (PFASs) are a group of compounds containing at least one fully fluorinated methyl or methylene carbon (without any H, Cl, Br, or I atoms). Due to their extremely high C-F bonds, these compounds possess chemical stability, hydrophobicity, oleophobicity, thermal stability, and surface activity. Consequently, they are widely used in over 200 areas of production and daily life, potentially causing irreversible impacts on the environment and human health.

[0003] The most extensive research on PFASs involves traditional perfluoroalkyl acids (PFAAs), which can be further divided into perfluoroalkyl carboxylates (PFCAs) and perfluoroalkyl sulfonates (PFSAs) based on the anionic groups that confer water solubility. Updates in production processes and the implementation of policies and regulations have led to the gradual phase-out of long-chain substances (PFCAs ≥ 8, PFSAs ≥ 7) in favor of shorter-chain, less toxic, and less bioaccumulative alternatives. Consequently, the occurrence of PFASs in various environmental water bodies has shifted to a mixed pattern of traditional and short-chain PFASs.

[0004] Drinking contaminated water is considered the main route of PFASs exposure for the general population. PFASs have the ability to cross the blood-brain barrier and increase the risk of neurotoxicity after accumulating in the brain. Evidence shows that there is a potential association between PFASs exposure and neurotoxic effects, including cognitive deficits, neurodevelopmental disorders, and neurodegenerative diseases. Currently, most toxicological studies focus on a single PFAS; moreover, the exposure concentrations used in these studies far exceed the pollution levels observed in the environment; in addition, the elucidation of the mechanism of PFASs-induced neurotoxicity remains incomplete. From a practical point of view, there is a serious lack of toxicity studies related to long-term, simultaneous, low-level exposure to various PFASs mixtures. Therefore, it is crucial to accurately identify the essential characteristics of neurotoxicity caused by PFASs exposure and to screen accurate and reliable early diagnostic markers.

[0005] Omics technologies are valuable for identifying biomolecular changes and clustering pathways of adverse outcomes caused by PFAS exposure. Exosomes are small, nanoscale vesicles containing biomarkers such as miRNAs. Transcriptomics-based studies of changes in exosomal miRNA expression levels can serve as biomarkers for monitoring exposure and adverse reactions, as epigenetic changes in biomarkers often precede measurable subclinical effects or the development of pathological conditions. Metabolomics, the omics technique closest to phenotypic characterization, focuses on the metabolic pathways of endogenous metabolites, their influence by genetic or environmental factors, and their temporal patterns. Metabolites are the terminal link in the regulation of biochemical activity, and analyzing metabolic changes in biological fluids can more directly and accurately reflect the body's pathophysiological state. Although omics technologies have been applied to the study of diagnostic biomarkers for various diseases and toxicological effects caused by PFASs, single-omics analyses are limited in their ability to reflect interactions between biomolecules and lack sufficient evidence to evaluate the efficacy of biomarkers.

[0006] In summary, in order to address the difficulties in early diagnosis and identification of neurotoxicity caused by PFASs exposure, transcriptomics and non-targeted metabolomics methods were used to detect differential miRNAs in rat plasma exosomes and differential metabolites in plasma, screen specific diagnostic markers, and explore related pathways of neurotoxicity, so as to provide a reference for the early diagnosis and identification of neurotoxicity caused by PFASs exposure. Summary of the Invention

[0007] To address the above deficiencies, the present invention provides markers for early diagnosis of neurotoxicity caused by PFASs exposure from the perspectives of transcriptomics and metabolomics.

[0008] The technical solution of the present invention is: In one aspect, the present invention provides a marker for identifying neurotoxicity caused by mixed exposure to perfluorinated compounds, comprising a miRNA combination and / or a metabolite combination; The miRNA combination includes rno-miR-221-3p, rno-miR-222-3p, rno-miR-1843b-5p, rno-mR-1843a-5p, rno-miR-21-5p, rno-miR-374-5p, rno-miR-328a-3p, rno-miR-3559-5p, rno-miR-1306-5p, rno-let-7b-5p, rno-let-7c-5p, rno-miR-28-3p, rno-let-7i-5p, rno-miR-223-5p, rno-miR-3064-5p, rno-let-7d-3p and rno-let-7f-5p; The metabolite combination includes Serol, AL-Threo-4-Hex-4-Enopyranuronosyl-D-Galacturonic Acid, 2-methylhippuric acid, xanthine, xanthine riboside, acetylglycine and Pc (P-16:0 / 4:0).

[0009] It should be noted that Pc(P-16:0 / 4:0) is the standard full name of the metabolite, and the bracketed part cannot be modified.

[0010] In another aspect, the present invention provides use of a reagent for detecting the aforementioned markers in a sample in the preparation of a product for identifying neurotoxicity caused by mixed exposure to perfluorinated compounds.

[0011] Specifically, the sample can be selected from whole blood, serum or plasma.

[0012] Preferably, the sample may be selected from plasma.

[0013] In another aspect, the present invention provides a method for identifying neurotoxicity caused by mixed exposure to perfluorinated compounds, by detecting the relative expression levels of the aforementioned markers to determine whether neurotoxicity has occurred.

[0014] Specifically, the relative expression detection method of the miRNA combination includes but is not limited to: real-time quantitative PCR, high-throughput sequencing, Northern blotting, microarray or digital PCR; the relative expression detection method of the metabolite combination includes but is not limited to: liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, nuclear magnetic resonance, chemical analysis or immunoassay.

[0015] Preferably, the method for detecting the relative expression of the miRNA combination may be real-time quantitative PCR; and the method for detecting the relative expression of the metabolite combination may be UHPLC-MS.

[0016] Specifically, the primers of the miRNA combination are shown as SEQ ID NO.1-SEQ ID NO.36.

[0017] Specifically, the liquid phase conditions of the UHPLC-MS are as follows: the chromatographic column is ACQUITY UPLC HSS T3, 100 mm × 2.1 mm, 1.8 μm; the mobile phase A is an aqueous solution containing 5% v / v acetonitrile and 0.1% v / v formic acid, and the mobile phase B is an organic phase containing 5% v / v water, 0.1% v / v formic acid, 47.5% v / v acetonitrile, and 47.5% v / v isopropanol; the elution program is as follows: positive ion mode: 0 min, 0% B; 0-3 min, 20% B; 3-4.5 min, 35% B; 4.5-5 min, 100% B; 5-6.3 min, 100% B; 6.3-8 min, 0% B; negative ion mode: 0 min, 0% B ; 0-1.5min, 5%B; 1.5-2min, 10%B; 2-4.5min, 30%B; 4.5-5min, 100%B; 5-6.3min, 100%B; 6.3-8min, 0%B; the injection volume was 3μL, the flow rate was 0.40mL / min, and the column temperature was 40℃.

[0018] The mass spectrometry conditions were as follows: mass scan range 70–1050 m / z, sheath gas flow rate 50 psi, auxiliary gas flow rate 13 psi, auxiliary gas heating temperature 425°C, positive mode ion spray voltage 3500 V, negative mode ion spray voltage −3500 V, S-Lens voltage 50 V, ion transfer tube temperature 325°C, normalized collision energy 20–40–60 V cyclic collision energy, primary mass spectrometry resolution 60,000, and secondary mass spectrometry resolution 7500.

[0019] In another aspect, the present invention provides a kit for identifying neurotoxicity caused by mixed exposure to perfluorochemicals, comprising reagents for detecting the relative expression levels of the aforementioned markers.

[0020] The beneficial effects of the present invention are: The biomarker combination of the present invention showed significant changes in all samples, demonstrating good diagnostic performance, suggesting its potential for early identification of neurotoxicity caused by exposure to mixed PFASs at environmentally relevant concentrations. The application value of the present multi-omics strategy combining exosome transcriptomics and plasma metabolomics in environmental health research provides new insights into the molecular diagnosis of neurotoxicity caused by PFASs exposure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1The results of the behavioral experiment are shown in Figure 1. A represents the discrimination index during the test phase of the novel location recognition experiment; B represents the escape latency during the navigation phase of the water maze experiment; C, D, and E represent the escape latency, time spent around the platform, and percentage of time in the four quadrants during the spatial exploration phase of the water maze experiment, respectively. p <0.05, compared with the control group; # p <0.05, compared with other experimental groups).

[0022] Figure 2 Shown are the histopathological changes of rats in the representative experimental groups. Figure 3 The statistical results of positive cells in the histopathological changes of rats in the representative experimental group (* p <0.05, compared with the control group).

[0023] Figure 4 GO functional pathways involved in exosome transcriptomics-specific miRNA target genes.

[0024] Figure 5 KEGG functional pathways involved in exosome transcriptomics-specific miRNA target genes.

[0025] Figure 6 qPCR validation results of some specific miRNAs in exosome transcriptomics (* p <0.05, compared with the control group).

[0026] Figure 7 This is a quality control chart for the raw data of non-targeted metabolomics. Figures A and B represent the PCA and PLS-DA score plots of the overall distribution of rat plasma samples in each group, respectively.

[0027] Figure 8 The figure shows the ROC curve of the diagnostic efficacy of the metabolite marker combination using the MIX_ultra group as an example. DETAILED DESCRIPTION

[0028] The present invention will be further clarified and fully described below by way of examples. The following examples are only a portion of the present invention and are not intended to limit the present invention, but are merely for illustration. The experimental methods used in the following examples are all routine experiments unless otherwise specified, and the materials and reagents used in the following examples are all commercially available unless otherwise specified.

[0029] Example 1 Experimental design and multi-omics analysis 1.1 Animal grouping and model establishment 1.1.1 Animal grouping Thirty 6-week-old Sprague-Dawley rats were selected and randomly divided into five groups (n=6) after one week of adaptive feeding: pure water group (control group), PFASs mixed exposure group (experimental group: 0.001 mg / kg body weight (bw), MIX_low group; 0.050 mg / kg bw, MIX_medium group; 0.250 mg / kg bw, MIX_high group; 7.000 mg / kg bw, MIX_ultra group).

[0030] 1.1.2 Model establishment The experimental rats were orally gavaged with a PFASs mixture every day (the proportions of each substance in the PFASs mixture are shown in Table 1) for 28 consecutive days, and the gavage dose (10 mL / kg bw) was adjusted every 7 days according to body weight.

[0031] Table 1 Proportions of various substances in PFASs mixture

[0032] Note: PFBA is perfluorobutyric acid; PFPeA is perfluoropentanoic acid; PFHxA is perfluorohexanoic acid; PFHpA is perfluoroheptanoic acid; PFOA is perfluorooctanoic acid; PFBus is perfluorobutane sulfonic acid; PFHxS is perfluorohexane sulfonic acid; PFOS is perfluorooctane sulfonic acid; ∑PFASs is the sum of 8 PFASs.

[0033] After the exposure, the open field test, novel location recognition test and water maze test were used to detect the rats' motor ability and spatial learning and memory ability.

[0034] 1.1.2.1 Open field test The open field experiment setup consists of a 40×40×40 black reaction box, an analysis camera, and a small animal behavior recording and analysis system. The specific experimental steps are as follows: Adaptation period: The rats were placed in the experimental environment to adapt for 1 hour.

[0035] During the spontaneous activity period, rats were placed in the reaction chamber. The system was used to time the rats for 10 minutes, recording and analyzing the distance and average speed of the rats in the central area of the open field chamber. Before each experiment, the rats' urine and feces were thoroughly cleaned and sprayed with 75% alcohol to eliminate rat odor.

[0036] 1.1.2.2 New Object Recognition Experiment The entire experimental process is divided into three stages: Familiarization period: One day before the experiment, each rat was placed in a 40×40×40 open field for 10 min. After the familiarization period, the rat was taken out and returned to the cage. The open field was wiped with 75% alcohol to remove the odor left by the rat.

[0037] During the training phase, two objects of identical shape and texture were placed parallel to each other on one side of the open field, 14 cm apart and 8 cm from the adjacent open field wall. Rats were placed from the midline of the other side of the open field, facing the open field wall. They were allowed to move freely for 10 minutes. At the end of the experiment, the rats were removed, their urine and feces were cleaned, and the open field was wiped with 75% alcohol to remove rat odor.

[0038] Test phase: 24 hours after the training phase, one object was placed on the opposite side of the open field, while the other object remained in the same position, so that the two objects were diagonally aligned. Rats were placed in the open field at the same location as during training, and the behavioral video tracking system recorded the time the rats spent exploring the two objects. The entire test lasted 10 minutes.

[0039] 1.1.2.3 Morris water maze test The Morris water maze experimental apparatus consists of a circular pool 160 cm in diameter and 50 cm high, with a platform 12 cm in diameter. The pool is divided into four quadrants (NE, NW, SW, SE) based on the four directions: east, south, west, and north (E, S, W, N). Each quadrant has a patterned interior to help the rats find their way, and light-colored curtains are hung around the perimeter. During experiments, the pool is filled with warm water at 18-23°C and dyed black with ink (white for the experimental rats to create contrast). This allows the video capture system to record the rats' swimming trajectories.

[0040] One day before the experiment, rats were placed in the water (excluding the platform) with their heads facing the wall and allowed to swim freely for 2 minutes to familiarize themselves with the water maze environment. The formal experiment was divided into two phases: the first five days were spent on navigation and the sixth day was spent on spatial exploration.

[0041] Positioning navigation experiment: Before the experiment began, the platform was placed in the target quadrant (SW), 1-2 cm below the water surface to prevent the rat from seeing the platform's location. At the beginning of the experiment, the experimenter randomly placed the rat into the water facing the inner wall of the pool from four entry points (E, N, NW, SE) and trained once each time, with a 30-min interval between training sessions.

[0042] If a rat climbed onto the platform after swimming for a while and remained there for more than 15 seconds, it was considered to have found the platform. The escape latency was calculated as the time from entering the water to climbing onto the platform with all four limbs. If the rat could not find the platform within 120 seconds, it was guided to the platform with a wooden stick and remained there for 15 seconds. The latency was calculated as 120 seconds, and the average of the four latencies was used as the rat's score for that period. After the experiment, the rat was wiped dry with a cloth and its hair was blown dry with a hair dryer before being returned to its cage.

[0043] Spatial exploration experiment: After the first phase of the experiment, the platform was removed and the rat was placed in the water from the quadrant opposite the platform (NE). The swimming trajectory of the rat within 2 min, the number of times the rat crossed the original platform, the percentage of time it stayed in the original platform quadrant, the total swimming distance, the swimming speed, and other related data were observed and recorded.

[0044] 1.1.3 Sample Collection (1) Blood The rats in each group were fasted for 12 hours. After successful anesthesia, the abdomen of the rats was disinfected with 75% alcohol cotton balls. A disposable blood collection needle was inserted into the abdominal aorta to collect blood in a vacuum blood collection tube for subsequent processing.

[0045] (2) Organization After blood samples were collected, cardiac perfusion was performed using normal saline and 4% paraformaldehyde. The whole brains of rats in each group were placed in 4% paraformaldehyde solution and fixed at room temperature to maintain their morphological structure.

[0046] 1.1.4 Sample preparation and instrument parameters 1.1.4.1 NovaSeq-based transcriptomics methods (1) Whole blood samples were drawn using EDTA-containing blood collection tubes and centrifuged at 1,900 g for 10 min at 4°C. The supernatant was collected as plasma.

[0047] (2) The obtained plasma was centrifuged again at 3,000 g for 15 min at 4°C, and the supernatant was carefully aspirated.

[0048] (3) The plasma was divided into EP tubes and stored at −80°C before sample delivery to avoid repeated freezing and thawing.

[0049] (4) Thaw the sample quickly at 37°C.

[0050] (5) Transfer the sample to a new centrifuge tube and centrifuge at 2,000 g for 30 min at 4°C.

[0051] (6) Carefully transfer the supernatant to a new centrifuge tube and centrifuge again at 10,000 g for 45 min at 4°C to remove larger vesicles.

[0052] (7) Take the supernatant, filter it through a 0.45 μm filter membrane, and collect the filtrate.

[0053] (8) Transfer the filtrate to a new centrifuge tube, select an ultraspeed rotor, and centrifuge at 100,000 g for 70 min at 4°C.

[0054] (9) Remove the supernatant, resuspend with 10 mL of pre-cooled 1× PBS, select an ultraspeed rotor, and ultracentrifuge again at 100,000 g for 70 min at 4°C.

[0055] (10) Remove the supernatant and resuspend in 300 μL of pre-chilled 1× PBS.

[0056] (11) Add 1 mL of Trizol to the exosome sample dissolved in PBS, shake it to fully lyse it, and let it stand at room temperature for 5 minutes.

[0057] (12) Centrifuge at 13,000 g for 5 min at 4°C and transfer the supernatant to another centrifuge tube. Add pre-cooled chloroform at a ratio of 0.2 mL chloroform / 1 mL Trizol, shake well, and let stand at room temperature for 5 min.

[0058] (13) After centrifugation at 13,000 g for 15 min at 4°C, aspirate the upper aqueous phase (do not let the pipette tip touch the middle protein layer) into another centrifuge tube (about 400 μL), add an equal volume of pre-cooled isopropanol, and let it stand at room temperature for 10 min.

[0059] (14) Centrifuge at 13,000 g for 10 min at 4°C and discard the supernatant. A small amount of white precipitate will be at the bottom of the tube (which may not be visible to the naked eye if the amount of RNA is very small). Add 1 mL of pre-cooled 75% ethanol to suspend the precipitate.

[0060] (15) Centrifuge at 12,000 g for 5 minutes at 4°C, discard the supernatant, and centrifuge the tube for a few seconds to remove the residual liquid hanging on the tube wall. Use a 10 μL pipette tip to absorb the residual liquid and let it dry at room temperature for 3-5 minutes. Be careful not to dry it too much, otherwise the RNA will be difficult to dissolve and may cause degradation.

[0061] (16) Add 20-50 μL (the amount added depends on the amount of RNA extracted) of sterile 0.1% DEPC water to dissolve.

[0062] (17) The NovaSeq Reagent Kit (NovaSeq X Plus, Illumina) was used to construct a library, and a high-throughput sequencing platform was used to sequence all the miRNAs enriched in serum exosomes, identify existing and newly discovered miRNAs, and screen miRNAs with significantly different expression levels.

[0063] (18) The extracted RNA was reverse transcribed using the RevertAid First Strand cDNA Synthesis Kit (K1691, Thermo), and specific miRNA expression was detected by qPCR.

[0064] 1.1.4.2 UHPLC-MS-based metabolomics methods Sample preparation: (1) Whole blood samples were collected using sodium heparin anticoagulant tubes, centrifuged at 3,000 rpm for 10 min at 4°C, and the upper layer was collected and aliquoted into 1.5 mL centrifuge tubes (0.2 mL / tube). The samples were quickly frozen in liquid nitrogen for 15 min and stored in a −80°C refrigerator.

[0065] (2) Accurately transfer 100µL of sample into a 1.5mL centrifuge tube.

[0066] (3) Add 300 μL of extract containing internal standard (methanol:acetonitrile = 1:1 (v:v)).

[0067] (4) Vortex mix for 30 seconds and perform low-temperature ultrasonic extraction for 30 minutes (5°C, 40 kHz).

[0068] (5) Place the sample at −20°C for 30 min.

[0069] (6) Centrifuge for 15 min (4°C, 13,000 g), remove the supernatant, and blow dry with nitrogen.

[0070] (7) Add 100 μL of reconstitution solution (acetonitrile: water = 1:1 (v:v)) to reconstitute.

[0071] (8) Vortex mix for 30 seconds and perform low-temperature ultrasonic extraction for 5 minutes (5°C, 40 kHz).

[0072] (9) Centrifuge for 10 min (4°C, 13,000 g), transfer the supernatant into an injection vial with an inner cannula and analyze on the analyzer.

[0073] (10) Take 20µL of supernatant from each sample and mix them as quality control samples.

[0074] Instrument parameters: (1) Chromatographic conditions The chromatographic column was an ACQUITY UPLC HSS T3 (100 mm × 2.1 mm id, 1.8 µm); mobile phase A was 95% v / v water + 5% v / v acetonitrile (containing 0.1% v / v formic acid), and mobile phase B was 47.5% v / v acetonitrile + 47.5% v / v isopropanol + 5% v / v water (containing 0.1% v / v formic acid); the elution program was as follows: positive ion mode: 0 min, 0% B; 0-3 min, 20% B; 3-4.5 min, 35% B; 4.5-5 min, 100% B; 5-6.3 min, 100% B; 6.3-8 min, 0% B; negative ion mode: 0 min, 0% B ; 0-1.5min, 5%B; 1.5-2min, 10%B; 2-4.5min, 30%B; 4.5-5min, 100%B; 5-6.3min, 100%B; 6.3-8min, 0%B; injection volume was 3μL; flow rate was 0.40mL / min; column temperature was 40℃.

[0075] (2) Mass spectrometry conditions The DDA scan mode was used to collect mass spectrometric signals in both positive and negative ion modes. The mass scan range was 70–1050 m / z. The sheath gas flow rate was 50 psi. The auxiliary gas flow rate was 13 psi. The auxiliary gas heating temperature was 425°C. The positive mode ion spray voltage was set to 3500 V. The negative mode ion spray voltage was set to −3500 V. The S-Lens voltage was set to 50 V. The ion transfer tube temperature was 325°C. The normalized collision energy was 20–40–60 V with a cyclic collision energy. The primary mass spectrometer resolution was 60,000. The secondary mass spectrometer resolution was 7500.

[0076] 1.5 Statistical analysis Statistical analyses were performed using SPSS 27.0. Unless otherwise indicated, data are presented as mean ± standard deviation. Data were assessed for normality and homogeneity of variance using the Shapiro-Wilk test and the Levene test, respectively. Normally distributed data with homogeneous variance were analyzed using the t-test or one-way analysis of variance (ANOVA) followed by the Tukey multiple comparison test. Nonnormally distributed data were analyzed using the Mann-Whitney U test or the Kruskal-Wallis H test. p <0.05, considered statistically significant.

[0077] 1.6 Model Construction Evaluation Results 1.6.1 Motor ability and spatial learning and memory ability of rats To assess the potential adverse effects of PFASs exposure on motor, learning, and memory functions, an open field test was first conducted to investigate potential differences in rats' motor levels. The results showed that there were no significant differences in the average speed of rats among the groups during the 10-minute observation period.

[0078] In the adaptation and familiarization phases of the novel location recognition experiment, there was no significant difference in the total exploration time of the two objects among all rats. The discrimination index of the MIX_ultra group rats in the test phase was significantly higher than that of the control group ( p <0.05), indicating that due to the rats' innate curiosity about new objects, the memory of the original location of the objects gradually weakened.

[0079] During the platform-visible phase of the water maze test, there were no significant differences in swimming speed or escape latency, indicating that the rats' motor and visual functions were sufficient to ensure effective subsequent experiments. During the five-day training period, the MIX_ultra group showed significant increases in escape latency on both the third and fifth days. During the platform-free spatial exploration phase, the MIX_ultra group showed significant increases in escape latency and time spent in the quadrant contralateral to the platform. Furthermore, the MIX_ultra group significantly decreased the time spent around the platform and in the platform and right quadrants.

[0080] These results ( Figure 1 ) Overall, it was shown that exposure to environmentally relevant concentrations of PFASs mixtures can lead to impairment of spatial learning and memory abilities in rats.

[0081] 1.6.2 Histological changes in rats in representative experimental groups To further investigate the neurotoxicity induced by PFASs, pathological changes in the hippocampus were assessed using hematoxylin-eosin and Nissl staining, and neuronal damage was assessed by TUNEL and immunohistochemistry. Furthermore, immunofluorescence was used to investigate the expression of Iba-1 and GFAP, markers of microglial and astrocyte activation, to further elucidate the effects of PFASs on brain inflammation.

[0082] HE staining showed that the neurons were atrophied, the nuclei were darkly stained, the basophilia was increased, the shape was irregular, a small number of neurons were vacuolated, and there was scattered inflammatory cell infiltration in the hippocampus. Nissl staining showed that the number of Nissl bodies in the MIX_ultra group decreased after exposure to PFASs. In addition, a significant increase in TUNEL-positive cells was observed in this group. Immunohistochemistry results showed that the expression of Neun in mature neurons in the MIX_ultra group was reduced, suggesting that neurons were damaged. As shown by the results of immunofluorescence, the number of Iba-1 and GFAP-positive cells in the MIX_ultra group increased significantly, indicating that glial cell activation was enhanced and hippocampal inflammation was aggravated ( Figure 2 and Figure 3).

[0083] Example 2 Screening and Verification of Potential Diagnostic Markers Multi-omics analysis was performed on the samples collected from the experimental rats in Example 1 to identify and verify potential diagnostic markers.

[0084] 2.1 Transcriptomic analysis of plasma exosome samples from rats in each group 2.1.1 Quality Control of Sequencing Data Raw sequencing data contains sequencing adapter sequences or low-quality reads. To ensure the accuracy of subsequent bioinformatics analysis, the raw data is first filtered to obtain high-quality data to ensure smooth subsequent analysis. After quality control, the length of clean reads is analyzed, and reads with a length of 18-32 nt are selected for subsequent analysis based on the characteristics of miRNAs.

[0085] 2.1.2 Alignment with the reference genome The percentage of Q30 bases in all samples was >95.71%, indicating that the accuracy of RNA-seq data was high. https: / / asia.ensembl.org / Rattus_norvegicus / Info / Index ) The comparison readings were between 1684306 and 6498023.

[0086] 2.1.3 Expression difference statistics The software used for differential expression was DESeq2, and the screening criteria included: (1) up / down fold difference (FoldChange) = 1.2, (2) p A p-value < 0.05 was used to analyze miRNA expression in the control group and each experimental group. Compared with the control group, 36, 72, 92, and 79 significantly differentially expressed miRNAs were identified in the MIX_low, MIX_medium, MIX_high, and MIX_ultra groups, respectively. Venn analysis of the differentially upregulated and downregulated miRNAs in each experimental group identified a total of 17 miRNAs associated with mixed PFAS exposure (Table 2).

[0087] Table 2 miRNAs that changed significantly in each experimental group

[0088] The primer sequences corresponding to the above 17 miRNAs are shown in Table 3.

[0089] Table 3 Primer sequences corresponding to miRNAs that changed significantly in each experimental group

[0090] Note: The reverse primer for the stem-loop qPCR method is universal primer R, with the sequence GTGCAGGGTCCGAGGT (SEQ ID NO. 35) or ATCCAGTGCAGGGTCCGAGG (SEQ ID NO. 36).

[0091] 2.1.4 GO and KEGG enrichment analysis of differential miRNA target genes Fisher's test was used as a statistical method, and when the corrected p value( p When the adjustment) < 0.05, the function was considered to be significantly enriched. GO-based functional enrichment analysis was performed on the target genes of differentially expressed miRNAs. The top 20 statistically significant ( p ≤0.05). At the biological process level, the differentially expressed miRNA target genes were mainly enriched in single-organism processes, single-organism cellular processes, and positive regulation of biological processes; at the cellular component level, they were mainly enriched in membrane-bounded organelles and intracellular membrane-bounded organelles; in addition, at the molecular function level, they were mainly enriched in protein binding, molecular function, and binding ( Figure 4 ).

[0092] The differentially expressed miRNA target genes were analyzed using the KEGG database. Figure 5 The top 20 statistically significant enriched pathways are shown ( p ≤0.05). These target genes were mainly involved in several key biological pathways, including MAPK signaling pathway, FoxO signaling pathway, axon regeneration, endocrine resistance, Wnt signaling pathway, cancer pathway, and cAMP signaling pathway.

[0093] 2.1.5 RT-qPCR verification of differential miRNAs Five differentially expressed miRNAs were selected for RT-qPCR validation. Overall, the qPCR results were consistent with the RNA-seq data. Figure 6As shown in the results, rno-miR-328a-3p and rno-let-7i-5p showed a significant decrease after PFASs exposure. Although rno-miR-221-3p, rno-miR-21-5p, and rno-let-7b-5p did not show statistical differences, they showed a trend corresponding to the sequencing results.

[0094] 2.2 Metabolomics analysis of plasma samples from rats in each group 2.2.1 Quality Control of Sample Data Total ion current (TIC) chromatograms are plotted with time on the horizontal axis and total ion intensity on the vertical axis. TIC plots in both positive and negative ion modes show that the quality control sample peaks are well-shaped and relatively evenly distributed.

[0095] Raw data were preprocessed, including filtering low-quality peaks, filling missing values, normalization, RSD assessment of QC samples, and data conversion. This process minimizes the impact of data variation unrelated to the study objectives on data analysis and facilitates the screening and analysis of potential differentially expressed metabolites of interest. Overall data were considered qualified if the RSD of the QC samples was <30% and the cumulative proportion of peaks was >0.7. In this experiment, the RSD of the QC samples was <30% and the cumulative proportion of peaks was >0.9, indicating that the data were qualified.

[0096] Unsupervised principal component analysis (PCA) was used to analyze the metabolic profiles of the plasma samples of the control group and each experimental group. The samples of each group showed a certain separation trend. Supervised partial least squares discriminant analysis (PLS-DA) was used to analyze the metabolic profiles of the plasma samples of each group. The samples of each group were further separated, indicating that there were certain differences in metabolites between the groups. The QC samples were well clustered together, indicating that the repeatability of the experiment was good ( Figure 7 A and B in ).

[0097] 2.2.2 Multivariate statistical analysis The present invention mainly focuses on the specific differences in endogenous metabolites between the control group and each experimental group. Therefore, the data of each group are then compared and analyzed pairwise to find the differential metabolites between the groups and analyze their changing trends.

[0098] PCA and PLS-DA analyses revealed a clear trend of metabolite separation between the experimental and control groups. Orthogonal partial least-squares discrimination analysis (OPLS-DA), a modification of PLS-DA, further reduced random errors within the groups and enhanced the differences between the groups. OPLS-DA score plots showed good separation of metabolites between the experimental and control groups.

[0099] 2.2.3 Screening of differential metabolites The screening criteria for differential metabolites include: (1) Fold Change = 1; (2) variable weight value (VIP) obtained by OPLS-DA model ≥ 1; (3) p <0.05.

[0100] Compared with the control group, a total of 41 endogenous metabolites underwent significant changes in the MIX_low group, of which the relative contents of 25 metabolites were increased and the relative contents of 16 metabolites were decreased; a total of 102 endogenous metabolites underwent significant changes in the MIX_medium group, of which the relative contents of 38 metabolites were increased and the relative contents of 64 metabolites were decreased; a total of 317 endogenous metabolites underwent significant changes in the MIX_high group, of which the relative contents of 83 metabolites were increased and the relative contents of 234 metabolites were decreased; a total of 419 endogenous metabolites underwent significant changes in the MIX_ultra group, of which the relative contents of 45 metabolites were increased and the relative contents of 374 metabolites were decreased.

[0101] Through Venn analysis, the metabolite information showing significant differences between each experimental group and the control group is shown in Table 4.

[0102] Table 4 Identification of differential metabolites in plasma samples among different groups

[0103] 2.2.4 Pathway enrichment analysis of differential metabolites The differential metabolites were annotated with metabolic pathways using the KEGG database to obtain the pathways in which the differential metabolites participated.

[0104] The metabolic pathways involved in the differential metabolites in the MIX_low group were mainly caffeine metabolism, neuroactive ligand-receptor interaction, nucleotide metabolism, FcγR-mediated phagocytosis and Apelin signaling pathway.

[0105] The metabolic pathways involved in the differential metabolites in the MIX_medium group were mainly: protein digestion and absorption, D-amino acid metabolism, mineral absorption, central carbon metabolism in cancer, and synaptic vesicle cycle.

[0106] The metabolic pathways involved in the differential metabolites in the MIX_high group were mainly bile secretion, PPAR signaling pathway, cofactor biosynthesis, caffeine metabolism and protein digestion and absorption.

[0107] The metabolic pathways involved in the differential metabolites in the MIX_ultra group were mainly bile secretion, caffeine metabolism, nucleotide metabolism, steroid hormone biosynthesis and PPAR signaling pathway.

[0108] 2.2.5 Confirmation of metabolic markers Each experimental group showed significantly different metabolites compared with the control group, and the receiver operating characteristic (ROC) curve was further used to determine whether these metabolites could serve as early diagnostic biomarkers for neurotoxicity caused by mixed exposure to environmentally relevant concentrations of PFASs.

[0109] Figure 8 The AUC (area under the curve) indicated in the figure is the area under the corresponding curve. When AUC>0.5, the closer the AUC is to 1, the better the diagnostic effect. (AUC has low accuracy when it is 0.5-0.7, has certain accuracy when it is 0.7-0.9, and has high accuracy when it is above 0.9.)

[0110] The ROC curve analysis results of each experimental group and the control group showed that the AUC values of the seven metabolites were all greater than 0.8, indicating that these seven metabolites can accurately distinguish between the control group and rats with PFASs-induced neurotoxicity, and can serve as potential early diagnostic biomarkers.

[0111] The above detailed description is a specific description of one feasible embodiment of the present invention and is not intended to limit the scope of the present invention. It should be noted that any equivalent implementation or modification that does not depart from the present invention should be included within the scope of the technical solution of the present invention. Therefore, the scope of protection of the patent of this invention should be based on the attached requirements.

Claims

1. A marker for identifying neurotoxicity caused by mixed exposure to perfluorinated compounds, characterized in that: including miRNA panels and / or metabolite panels; The miRNA combination includes rno-miR-221-3p, rno-miR-222-3p, rno-miR-1843b-5p, rno-mR-1843a-5p, rno-miR-21-5p, rno-miR-374-5p, rno-miR-328a-3p, rno-miR-3559-5p, rno-miR-1306-5p, rno-let-7b-5p, rno-let-7c-5p, rno-miR-28-3p, rno-let-7i-5p, rno-miR-223-5p, rno-miR-3064-5p, rno-let-7d-3p and rno-let-7f-5p; The metabolite combination includes Serol, AL-Threo-4-Hex-4-Enopyranuronosyl-D-Galacturonic Acid, 2-methylhippuric acid, xanthine, xanthine riboside, acetylglycine and Pc (P-16:0 / 4:0).

2. Use of a reagent for detecting the marker of claim 1 in a sample in the preparation of a product for identifying neurotoxicity caused by mixed exposure to perfluorinated compounds.

3. The use according to claim 2, characterized in that The sample is selected from whole blood, serum or plasma.

4. The use according to claim 3, characterized in that The sample is plasma.

5. A method for identifying neurotoxicity caused by mixed exposure to perfluorinated compounds, characterized in that: Whether neurotoxicity is caused is determined by detecting the relative expression level of the marker described in claim 1.

6. The method according to claim 5, characterized in that Methods for detecting the relative expression of the miRNA combination include real-time quantitative PCR, high-throughput sequencing, Northern blotting, microarray or digital PCR; methods for detecting the relative expression of the metabolite combination include liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, nuclear magnetic resonance, chemical analysis or immunoassay.

7. The method according to claim 6, characterized in that The relative expression amount detection method of the miRNA combination is real-time quantitative PCR; the relative expression amount detection method of the metabolite combination is UHPLC-MS.

8. The method according to claim 7, characterized in that The primers of the miRNA combination are shown as SEQ ID NO.1 to SEQ ID NO.

36.

9. The method according to claim 7, characterized in that The UHPLC-MS liquid phase conditions were as follows: an ACQUITY UPLC HSS T3 column, 100 mm × 2.1 mm, 1.8 μm; mobile phase A was an aqueous solution containing 5% v / v acetonitrile and 0.1% v / v formic acid; mobile phase B was an organic phase containing 5% v / v water, 0.1% v / v formic acid, 47.5% v / v acetonitrile, and 47.5% v / v isopropanol; the elution program was as follows: positive ion mode: 0 min, 0% B; 0-3 min, 20% B; 3-4.5 min, 35% B; 4.5-5 min, 100% B; 5-6.3 min, 100% B; 6.3-8 min, 0% B; negative ion mode: 0 min, 0% B ; 0-1.5 min, 5% B; 1.5-2 min, 10% B; 2-4.5 min, 30% B; 4.5-5 min, 100% B; 5-6.3 min, 100% B; 6.3-8 min, 0% B; injection volume: 3 μL, flow rate: 0.40 mL / min, column temperature: 40°C; The mass spectrometry conditions were as follows: mass scan range 70–1050 m / z, sheath gas flow rate 50 psi, auxiliary gas flow rate 13 psi, auxiliary gas heating temperature 425°C, positive mode ion spray voltage 3500 V, negative mode ion spray voltage −3500 V, S-Lens voltage 50 V, ion transfer tube temperature 325°C, normalized collision energy 20–40–60 V cyclic collision energy, primary mass spectrometry resolution 60,000, and secondary mass spectrometry resolution 7500.

10. A kit for identifying neurotoxicity caused by mixed exposure to perfluorinated compounds, characterized in that: Comprising a reagent for detecting the relative expression amount of the marker according to claim 1.

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